Motion forecasting approaches are highly diverse in their input representations. Several methods, such as [1]}, [2]}, found success representing the input scene as an image and using convolutional neural networks to generate a scene representation. More recently, vectorized representations used in [3]}, [4]}, [5]}, [6]} have found success due to computationally efficient sparse representation and ability to model long-range dependencies. [7]}, [1]}, [9]}, [10]}, [11]} represent lanes as nodes in a graph to make use of the connectivity information. There are several output representations and loss functions in use as well. [12]}, [7]} predict an unconstrained regressed output and use a simple regression loss. [1]}, [9]}, [10]} predict an unconstrained heatmap with a cross entropy loss, sample the trajectory endpoint from the heatmap, and they complete the whole trajectory conditioned on the endpoint.
